Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- moe
+- fp8
+- vllm
+---
+
+# Mixtral-8x7B-Instruct-v0.1-FP8
+
+## Model Overview
+- **Model Architecture:** Mixtral-8x7B-Instruct-v0.1
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **Release Date:** 3/6/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).
+It achieves an average score of <TODO> on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves <TODO>.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations to FP8 data type, ready for inference with vLLM.
+This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized, except the MLP routers. 
+
+## Deployment
+
+### Use with vLLM
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+max_model_len, tp_size = 4096, 4
+model_name = "neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
+sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
+
+messages_list = [
+    [{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
+]
+
+prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
+
+outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
+
+generated_text = [output.outputs[0].text for output in outputs]
+print(generated_text)
+```
+
+vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+
+## Creation
+
+This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below with the following command:
+
+```bash
+python quantize.py --model_path mistralai/Mixtral-8x7B-Instruct-v0.1 --quant_path "output_dir" --calib_size 128 
+```
+
+
+```python
+import argparse
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.transformers import oneshot
+from llmcompressor.transformers.compression.helpers import calculate_offload_device_map
+import torch
+import os
+
+
+def main():
+    # Set up command line argument parsing
+    parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8')
+    parser.add_argument('--model_id', type=str, required=True,
+                        help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-8B-Instruct")')
+    parser.add_argument('--save_path', type=str, default='.',
+                        help='Custom path to save the quantized model. If not provided, will use model_name-FP8')
+    parser.add_argument('--calib_size', type=int, default=256)
+    args = parser.parse_args()
+
+    device_map = calculate_offload_device_map(
+        args.model_id,
+        reserve_for_hessians=False,
+        num_gpus=torch.cuda.device_count(),
+        trust_remote_code=True,
+        torch_dtype=torch.bfloat16,
+    )
+
+    model = AutoModelForCausalLM.from_pretrained(
+        args.model_id, device_map=device_map, torch_dtype=torch.bfloat16, trust_remote_code=True,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(args.model_id)
+
+    NUM_CALIBRATION_SAMPLES = args.calib_size
+    DATASET_ID = "garage-bAInd/Open-Platypus"
+    DATASET_SPLIT = "train"
+    ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
+    ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
+
+    def preprocess(example):
+        concat_txt = example["instruction"] + "\n" + example["output"]
+        return {"text": concat_txt}
+
+    ds = ds.map(preprocess)
+
+    def tokenize(sample):
+        return tokenizer(
+            sample["text"],
+            padding=False,
+            truncation=False,
+            add_special_tokens=True,
+        )
+
+    ds = ds.map(tokenize, remove_columns=ds.column_names)
+
+    # Configure the quantization algorithm and scheme
+    recipe = QuantizationModifier(
+        targets="Linear", scheme="FP8", ignore=["lm_head", "re:.*block_sparse_moe.gate"]
+    )
+
+    # Apply quantization
+    oneshot(
+        model=model,
+        dataset=ds,
+        recipe=recipe,
+        num_calibration_samples=args.calib_size
+    )
+
+    save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8")
+    os.makedirs(save_path, exist_ok=True)
+
+    # Save to disk in compressed-tensors format
+    model.save_pretrained(save_path, save_compressed=True, skip_compression_stats=True)
+    tokenizer.save_pretrained(save_path)
+    print(f"Model and tokenizer saved to: {save_path}")
+
+if __name__ == "__main__":
+    main()
+```
+
+## Evaluation
+
+The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard) using the following command:
+
+OpenLLM Leaderboard V1:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=8,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
+  --tasks openllm \
+  --write_out \
+  --batch_size auto \
+  --output_path output_dir \
+  --show_config
+```
+
+
+### Accuracy
+
+#### OpenLLM Leaderboard V1 evaluation scores
+
+| Metric                                   | mistralai/Mixtral-8x7B-Instruct-v0.1             | neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8 |
+|-----------------------------------------|:---------------------------------:|:-------------------------------------------:|
+| ARC-Challenge (Acc-Norm, 25-shot)       |       <TODO>                     |                 <TODO>                       |
+| GSM8K (Strict-Match, 5-shot)            |         <TODO>                    |                 <TODO>                        |
+| HellaSwag (Acc-Norm, 10-shot)           |         <TODO>                    |                 <TODO>                       |
+| MMLU (Acc, 5-shot)                      |         <TODO>                    |                 <TODO>                       |
+| TruthfulQA (MC2, 0-shot)                |          <TODO>                   |                 <TODO>                       |
+| Winogrande (Acc, 5-shot)                |          <TODO>                   |                 <TODO>                       |
+| **Average Score**                       | **<TODO>**                        | **<TODO>**                                   |
+| **Recovery (%)**                            | **100.00**                       | **<TODO>**                                   |
+